Top-Gate Transparent Organic Synaptic Transistors Based on Co-Mingled Heterojunctions

The rapid development of electronics and materials science has driven the progress of various electronic devices, and the new generation of electronic devices, represented by wearable smart products, has introduced transparent new demands on the devices. The ability of biological synapses to enhance or inhibit information when it is transmitted is thought to be the biological mechanism of artificial synaptic devices. The advantage of the human brain over conventional computers is the ability to perform efficient parallel operations when dealing with unstructured and complex problems. Inspired by biologically powerful neural networks, it is important to simulate biological synaptic functions on a single electronic device, and organic artificial synaptic transistors are artificially intelligent and very suitable artificial synaptic devices. Therefore, this paper proposes an organic artificial synaptic transistor with transparency (≥75%), provides a new solution for transparent top-gate synapses, and shows their promise for the next generation of organic electronics

Meta-analysis of Artificial Intelligence-Assisted Pathology for the Detection of Early Cervical Cancer

Abstract The objective of this study is to evaluate the accuracy of AI in the diagnosis of early cervical cancer using a systematic evaluation/meta-analysis approach and a comprehensive search of published literature. A comprehensive computer search of foreign language databases such as PubMed/MEDLINE, Embase, Cochrane Library, and IEEE; and Chinese databases such as China Knowledge Network, Wan fang Data Knowledge Platform, and Wipu.com (VIP) was conducted to retrieve reports on diagnostic accuracy of AI in early cervical cancer included between 1946 and December 2022. The literature was screened according to inclusion and exclusion criteria, and the quality of the included literature was evaluated using the QUADAS-2 quality evaluation chart. 2 × 2 diagnostic data in text were extracted and complete data were calculated using Review Manager 5.3. Heterogeneity between studies was analyzed using Stata SE 15.0 software with Meta Di Sc 1.4 and causes of heterogeneity were sought. A total of 42 data sets were included in the study of AI for the identification of benign and malignant cervical vitreous nodules, with a combined Sen value of 0.90; a combined Spe value of 0.90; a combined + LR value of 9.0; a combined −LR combined value was 0.11; DOR combined value was 83; and AUC was 0.96. The Fagan plot suggested a 50% pre-test probability and a 90% post-test probability of confirming diagnosis when the AI model diagnosed a glassy nodule positively, and a 10% probability of misdiagnosing the nodule when the result was negative. A total of 34 data sets were included in the study to determine benignity and malignancy of solid cervical nodules by AI, showing a combined Sen value of 0.92; a combined Spe value of 0.93; a combined + LR value of 13.37; a combined −LR combined value of 0.08; DOR combined value of 164; AUC of 0.97. The Fagan plot suggested a 50% pre-test probability and a 93% post-test probability of confirming the diagnosis of a solid cervical nodule when the AI model was positive, and an 8% probability of misdiagnosing the nodule when the result was negative. The results of likelihood ratio dot plots suggest that the use of an AI model for cervical detection in the clinical setting has a good exclusionary diagnostic power. Summing up the accuracy and specificity of the A1 model for diagnosis of early cervical cancer, accuracy for diagnosis of solid cervical nodules (0.90) > diagnosis of cervical nodules (0.92), and specificity for diagnosis of solid cervical nodules (0.90) > diagnosis of cervical nodules (0.93). The AI model is highly accurate in diagnosing early cervical cancer and has high clinical diagnostic value. The accuracy of the AI model in diagnosing solid nodules in the cervical region was higher than diagnosing ground glass nodules in the cervical region. The labeling method, image pre-processing method, and feature learning method affected the accuracy of the AI model in diagnosing early cervical cancer, while the choice of learning image library and validation database did not usually affect the accuracy of the model

Parishin treatment alleviates cardiac aging in naturally aged mice

Background: Cardiac aging progressively decreases physiological function and drives chronic/degenerative aging-related heart diseases. Therefore, it is crucial to postpone the aging process of heart and create products that combat aging. Aims & methods: The objective of this study is to examine the effects of parishin, a phenolic glucoside isolated from traditional Chinese medicine Gastrodia elata, on anti-aging and its underlying mechanism. To assess the senescent biomarkers, cardiac function, cardiac weight/body weight ratio, cardiac transcriptomic changes, and cardiac histopathological features, heart tissue samples were obtained from young mice (12 weeks), aged mice (19 months) treated with parishin, and aged mice that were not treated. Results: Parishin treatment improved cardiac function, ameliorated aging-induced cardiac injury, hypertrophy, and fibrosis, decreased cardiac senescence biomarkers p16Ink4a, p21Cip1, and IL-6, and increased the “longevity factor” SIRT1 expression in heart tissue. Furthermore, the transcriptomic analysis demonstrated that parishin treatment alleviated the cardiac aging-related Gja1 downregulation and Cyp2e1, Ccna2, Cdca3, and Fgf12 upregulation in the heart tissues. The correlation analysis suggested a strong connection between the anti-aging effect of parishin and its regulation of gut microbiota and metabolism in the aged intestine. Conclusion: The present study demonstrates the protective role and underlying mechanism of parishin against cardiac aging in naturally aged mice

Identification of N-(quinolin-8-yl)benzenesulfonamides as agents capable of down-regulating NFκB activity within two separate high-throughput screens of NFκB activation

We describe here a series of N-(quinolin-8-yl)benzenesulfonamides capable of suppressing the NFκB pathway identified from two high-throughput screens run at two centers of the NIH Molecular Libraries Initiative. These small molecules were confirmed in both primary and secondary assays of NFκB activation and expanded upon through analogue synthesis. The series exhibited potencies in the cell-based assays at as low as 0.6 μM, and several indications suggest that the targeted activity lies within a common region of the NFκB pathway